Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [16]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [17]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[17]:
<matplotlib.image.AxesImage at 0x7f3383105c88>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [18]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[18]:
<matplotlib.image.AxesImage at 0x7f339a4f6f28>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [19]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [20]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function

    input_real = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], "input_real")
    input_z = tf.placeholder(tf.float32, [None, z_dim], "input_z")
    learning_rate = tf.placeholder(tf.float32, None, "learning_rate")

    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [21]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    with tf.variable_scope('discriminator', reuse=reuse):
        
        alpha = 0.2
        
        h1 = tf.layers.conv2d(images, 64, 5, 2, 'same')
        h1 = tf.maximum(alpha * h1, h1)
        
        h2 = tf.layers.conv2d(h1, 128, 5, 2, 'same')
        h2 = tf.layers.batch_normalization(h2, training=True)
        h2 = tf.maximum(alpha * h2, h2)
        
        h3 = tf.layers.conv2d(h2, 256, 5, 2, 'same')
        h3 = tf.layers.batch_normalization(h3, training=True)
        h3 = tf.maximum(alpha * h3, h3)
        
        flat = tf.reshape(h3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [22]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.2
    
        h1 = tf.layers.dense(z, 2*2*512)
        h1 = tf.reshape(h1, (-1, 2, 2, 512))
        h1 = tf.layers.batch_normalization(h1, training=is_train)
        h1 = tf.maximum(alpha * h1, h1)
    
        h2 = tf.layers.conv2d_transpose(h1, 256, 5, 2, 'valid')
        h2 = tf.layers.batch_normalization(h2, training=is_train)
        h2 = tf.maximum(alpha * h2, h2)
    
        h3 = tf.layers.conv2d_transpose(h2, 128, 5, 2, 'same')
        h3 = tf.layers.batch_normalization(h3, training=is_train)
        h3 = tf.maximum(alpha * h3, h3)
        
        h4 = tf.layers.conv2d_transpose(h3, 64, 5, 1, 'same')
        h4 = tf.layers.batch_normalization(h4, training=is_train)
        h4 = tf.maximum(alpha * h4, h4)
    
        logits = tf.layers.conv2d_transpose(h4, out_channel_dim, 5, 2, 'same')
        out = tf.tanh(logits)
    
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [23]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_logits_real) * 0.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_logits_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [24]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    
    with tf.control_dependencies(update_ops):
        t_vars = tf.trainable_variables()
        
        d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
        g_vars = [var for var in t_vars if var.name.startswith('generator')]

        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

        return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [25]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [26]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)

    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])

    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            steps = 0
            for batch_images in get_batches(batch_size):
                steps +=1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, show_n_images, input_z, data_shape[3], data_image_mode)
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [28]:
batch_size = 32
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 10... Discriminator Loss: 0.7505... Generator Loss: 1.2688
Epoch 1/2... Batch 20... Discriminator Loss: 0.4413... Generator Loss: 2.7567
Epoch 1/2... Batch 30... Discriminator Loss: 0.4282... Generator Loss: 5.3384
Epoch 1/2... Batch 40... Discriminator Loss: 4.0415... Generator Loss: 0.0300
Epoch 1/2... Batch 50... Discriminator Loss: 0.8429... Generator Loss: 4.1427
Epoch 1/2... Batch 60... Discriminator Loss: 0.9482... Generator Loss: 3.3324
Epoch 1/2... Batch 70... Discriminator Loss: 0.7251... Generator Loss: 4.5799
Epoch 1/2... Batch 80... Discriminator Loss: 0.7111... Generator Loss: 7.7268
Epoch 1/2... Batch 90... Discriminator Loss: 0.7433... Generator Loss: 1.9877
Epoch 1/2... Batch 100... Discriminator Loss: 0.4674... Generator Loss: 4.1991
Epoch 1/2... Batch 110... Discriminator Loss: 0.6360... Generator Loss: 2.6677
Epoch 1/2... Batch 120... Discriminator Loss: 0.5773... Generator Loss: 5.4253
Epoch 1/2... Batch 130... Discriminator Loss: 0.5451... Generator Loss: 2.3956
Epoch 1/2... Batch 140... Discriminator Loss: 0.5469... Generator Loss: 2.4308
Epoch 1/2... Batch 150... Discriminator Loss: 0.4486... Generator Loss: 2.7370
Epoch 1/2... Batch 160... Discriminator Loss: 0.5529... Generator Loss: 3.2417
Epoch 1/2... Batch 170... Discriminator Loss: 0.4196... Generator Loss: 4.1482
Epoch 1/2... Batch 180... Discriminator Loss: 0.4128... Generator Loss: 3.2963
Epoch 1/2... Batch 190... Discriminator Loss: 0.5428... Generator Loss: 2.5171
Epoch 1/2... Batch 200... Discriminator Loss: 1.0104... Generator Loss: 0.8547
Epoch 1/2... Batch 210... Discriminator Loss: 0.4446... Generator Loss: 4.7025
Epoch 1/2... Batch 220... Discriminator Loss: 0.6588... Generator Loss: 1.8037
Epoch 1/2... Batch 230... Discriminator Loss: 0.4581... Generator Loss: 2.8696
Epoch 1/2... Batch 240... Discriminator Loss: 0.4065... Generator Loss: 3.2426
Epoch 1/2... Batch 250... Discriminator Loss: 0.4370... Generator Loss: 3.4590
Epoch 1/2... Batch 260... Discriminator Loss: 0.4873... Generator Loss: 2.7260
Epoch 1/2... Batch 270... Discriminator Loss: 0.4314... Generator Loss: 2.9823
Epoch 1/2... Batch 280... Discriminator Loss: 0.4101... Generator Loss: 3.5872
Epoch 1/2... Batch 290... Discriminator Loss: 0.5026... Generator Loss: 2.2860
Epoch 1/2... Batch 300... Discriminator Loss: 0.5297... Generator Loss: 2.6560
Epoch 1/2... Batch 310... Discriminator Loss: 0.5031... Generator Loss: 2.4894
Epoch 1/2... Batch 320... Discriminator Loss: 0.4191... Generator Loss: 3.3878
Epoch 1/2... Batch 330... Discriminator Loss: 0.4364... Generator Loss: 5.0682
Epoch 1/2... Batch 340... Discriminator Loss: 0.4203... Generator Loss: 3.3449
Epoch 1/2... Batch 350... Discriminator Loss: 0.3973... Generator Loss: 3.5401
Epoch 1/2... Batch 360... Discriminator Loss: 0.4595... Generator Loss: 3.1056
Epoch 1/2... Batch 370... Discriminator Loss: 0.4008... Generator Loss: 4.3211
Epoch 1/2... Batch 380... Discriminator Loss: 0.5030... Generator Loss: 2.7913
Epoch 1/2... Batch 390... Discriminator Loss: 0.4201... Generator Loss: 3.4328
Epoch 1/2... Batch 400... Discriminator Loss: 0.4481... Generator Loss: 2.8968
Epoch 1/2... Batch 410... Discriminator Loss: 0.4890... Generator Loss: 2.3811
Epoch 1/2... Batch 420... Discriminator Loss: 0.6582... Generator Loss: 3.7380
Epoch 1/2... Batch 430... Discriminator Loss: 0.4444... Generator Loss: 3.1248
Epoch 1/2... Batch 440... Discriminator Loss: 0.6833... Generator Loss: 1.6266
Epoch 1/2... Batch 450... Discriminator Loss: 0.7105... Generator Loss: 4.6002
Epoch 1/2... Batch 460... Discriminator Loss: 0.4978... Generator Loss: 2.6257
Epoch 1/2... Batch 470... Discriminator Loss: 0.5689... Generator Loss: 2.3056
Epoch 1/2... Batch 480... Discriminator Loss: 0.5362... Generator Loss: 2.7955
Epoch 1/2... Batch 490... Discriminator Loss: 0.4287... Generator Loss: 3.2232
Epoch 1/2... Batch 500... Discriminator Loss: 0.5926... Generator Loss: 6.0497
Epoch 1/2... Batch 510... Discriminator Loss: 0.5191... Generator Loss: 3.1005
Epoch 1/2... Batch 520... Discriminator Loss: 0.5630... Generator Loss: 2.4463
Epoch 1/2... Batch 530... Discriminator Loss: 0.5509... Generator Loss: 3.1846
Epoch 1/2... Batch 540... Discriminator Loss: 0.4689... Generator Loss: 2.7955
Epoch 1/2... Batch 550... Discriminator Loss: 0.4484... Generator Loss: 2.8593
Epoch 1/2... Batch 560... Discriminator Loss: 0.4400... Generator Loss: 3.2528
Epoch 1/2... Batch 570... Discriminator Loss: 0.4197... Generator Loss: 3.9591
Epoch 1/2... Batch 580... Discriminator Loss: 0.4533... Generator Loss: 3.1588
Epoch 1/2... Batch 590... Discriminator Loss: 0.4845... Generator Loss: 2.8100
Epoch 1/2... Batch 600... Discriminator Loss: 0.6001... Generator Loss: 2.2878
Epoch 1/2... Batch 610... Discriminator Loss: 0.4265... Generator Loss: 3.0722
Epoch 1/2... Batch 620... Discriminator Loss: 0.9202... Generator Loss: 7.6476
Epoch 1/2... Batch 630... Discriminator Loss: 0.5159... Generator Loss: 2.7732
Epoch 1/2... Batch 640... Discriminator Loss: 0.5518... Generator Loss: 2.4633
Epoch 1/2... Batch 650... Discriminator Loss: 0.4220... Generator Loss: 3.6925
Epoch 1/2... Batch 660... Discriminator Loss: 0.7086... Generator Loss: 1.4475
Epoch 1/2... Batch 670... Discriminator Loss: 0.5738... Generator Loss: 2.2450
Epoch 1/2... Batch 680... Discriminator Loss: 0.9899... Generator Loss: 1.1531
Epoch 1/2... Batch 690... Discriminator Loss: 0.5331... Generator Loss: 2.3553
Epoch 1/2... Batch 700... Discriminator Loss: 0.5419... Generator Loss: 2.7607
Epoch 1/2... Batch 710... Discriminator Loss: 0.8552... Generator Loss: 1.2402
Epoch 1/2... Batch 720... Discriminator Loss: 0.4862... Generator Loss: 2.3794
Epoch 1/2... Batch 730... Discriminator Loss: 0.6460... Generator Loss: 1.7395
Epoch 1/2... Batch 740... Discriminator Loss: 0.6441... Generator Loss: 2.6139
Epoch 1/2... Batch 750... Discriminator Loss: 0.6136... Generator Loss: 2.2549
Epoch 1/2... Batch 760... Discriminator Loss: 0.7101... Generator Loss: 3.3470
Epoch 1/2... Batch 770... Discriminator Loss: 0.7095... Generator Loss: 1.5898
Epoch 1/2... Batch 780... Discriminator Loss: 0.5608... Generator Loss: 3.2644
Epoch 1/2... Batch 790... Discriminator Loss: 0.6426... Generator Loss: 1.6454
Epoch 1/2... Batch 800... Discriminator Loss: 0.5367... Generator Loss: 2.7214
Epoch 1/2... Batch 810... Discriminator Loss: 0.5554... Generator Loss: 2.3798
Epoch 1/2... Batch 820... Discriminator Loss: 0.5495... Generator Loss: 2.5023
Epoch 1/2... Batch 830... Discriminator Loss: 0.7666... Generator Loss: 1.3513
Epoch 1/2... Batch 840... Discriminator Loss: 0.6110... Generator Loss: 2.0725
Epoch 1/2... Batch 850... Discriminator Loss: 0.9280... Generator Loss: 1.0641
Epoch 1/2... Batch 860... Discriminator Loss: 0.6097... Generator Loss: 2.2982
Epoch 1/2... Batch 870... Discriminator Loss: 0.7027... Generator Loss: 2.8333
Epoch 1/2... Batch 880... Discriminator Loss: 1.2760... Generator Loss: 4.8655
Epoch 1/2... Batch 890... Discriminator Loss: 0.6736... Generator Loss: 2.3520
Epoch 1/2... Batch 900... Discriminator Loss: 0.5749... Generator Loss: 2.0281
Epoch 1/2... Batch 910... Discriminator Loss: 0.6644... Generator Loss: 2.3319
Epoch 1/2... Batch 920... Discriminator Loss: 0.7824... Generator Loss: 1.4281
Epoch 1/2... Batch 930... Discriminator Loss: 0.6854... Generator Loss: 1.6806
Epoch 1/2... Batch 940... Discriminator Loss: 0.5130... Generator Loss: 2.7912
Epoch 1/2... Batch 950... Discriminator Loss: 1.3053... Generator Loss: 2.8048
Epoch 1/2... Batch 960... Discriminator Loss: 0.8254... Generator Loss: 1.6926
Epoch 1/2... Batch 970... Discriminator Loss: 0.9650... Generator Loss: 1.3039
Epoch 1/2... Batch 980... Discriminator Loss: 1.1975... Generator Loss: 0.7648
Epoch 1/2... Batch 990... Discriminator Loss: 1.0314... Generator Loss: 1.3389
Epoch 1/2... Batch 1000... Discriminator Loss: 0.6983... Generator Loss: 1.6933
Epoch 1/2... Batch 1010... Discriminator Loss: 0.6927... Generator Loss: 1.6562
Epoch 1/2... Batch 1020... Discriminator Loss: 0.6414... Generator Loss: 1.7918
Epoch 1/2... Batch 1030... Discriminator Loss: 0.6818... Generator Loss: 2.0077
Epoch 1/2... Batch 1040... Discriminator Loss: 0.6537... Generator Loss: 1.7716
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Epoch 2/2... Batch 1810... Discriminator Loss: 0.9499... Generator Loss: 1.0333
Epoch 2/2... Batch 1820... Discriminator Loss: 0.9670... Generator Loss: 0.8929
Epoch 2/2... Batch 1830... Discriminator Loss: 0.7748... Generator Loss: 1.7585
Epoch 2/2... Batch 1840... Discriminator Loss: 0.9977... Generator Loss: 0.8919
Epoch 2/2... Batch 1850... Discriminator Loss: 1.1949... Generator Loss: 0.6345
Epoch 2/2... Batch 1860... Discriminator Loss: 1.1743... Generator Loss: 0.6842
Epoch 2/2... Batch 1870... Discriminator Loss: 0.9428... Generator Loss: 1.0897

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [30]:
batch_size = 32
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 10... Discriminator Loss: 1.2880... Generator Loss: 0.7758
Epoch 1/1... Batch 20... Discriminator Loss: 0.5563... Generator Loss: 3.5431
Epoch 1/1... Batch 30... Discriminator Loss: 0.4446... Generator Loss: 5.3367
Epoch 1/1... Batch 40... Discriminator Loss: 0.9549... Generator Loss: 0.8454
Epoch 1/1... Batch 50... Discriminator Loss: 0.4296... Generator Loss: 4.3731
Epoch 1/1... Batch 60... Discriminator Loss: 0.7035... Generator Loss: 4.4742
Epoch 1/1... Batch 70... Discriminator Loss: 1.2590... Generator Loss: 6.7956
Epoch 1/1... Batch 80... Discriminator Loss: 0.6181... Generator Loss: 5.2890
Epoch 1/1... Batch 90... Discriminator Loss: 0.8598... Generator Loss: 3.3936
Epoch 1/1... Batch 100... Discriminator Loss: 0.7776... Generator Loss: 1.6617
Epoch 1/1... Batch 110... Discriminator Loss: 0.5448... Generator Loss: 2.0048
Epoch 1/1... Batch 120... Discriminator Loss: 0.7991... Generator Loss: 1.6122
Epoch 1/1... Batch 130... Discriminator Loss: 1.3269... Generator Loss: 0.7559
Epoch 1/1... Batch 140... Discriminator Loss: 0.9863... Generator Loss: 1.5849
Epoch 1/1... Batch 150... Discriminator Loss: 0.6998... Generator Loss: 1.8099
Epoch 1/1... Batch 160... Discriminator Loss: 1.1065... Generator Loss: 0.6968
Epoch 1/1... Batch 170... Discriminator Loss: 1.5362... Generator Loss: 0.4097
Epoch 1/1... Batch 180... Discriminator Loss: 1.0458... Generator Loss: 1.0460
Epoch 1/1... Batch 190... Discriminator Loss: 0.6458... Generator Loss: 2.1555
Epoch 1/1... Batch 200... Discriminator Loss: 1.0375... Generator Loss: 0.9179
Epoch 1/1... Batch 210... Discriminator Loss: 0.8534... Generator Loss: 2.0522
Epoch 1/1... Batch 220... Discriminator Loss: 0.4794... Generator Loss: 3.6016
Epoch 1/1... Batch 230... Discriminator Loss: 0.7443... Generator Loss: 1.4381
Epoch 1/1... Batch 240... Discriminator Loss: 0.7481... Generator Loss: 1.3493
Epoch 1/1... Batch 250... Discriminator Loss: 0.8904... Generator Loss: 1.7820
Epoch 1/1... Batch 260... Discriminator Loss: 0.5985... Generator Loss: 3.1717
Epoch 1/1... Batch 270... Discriminator Loss: 0.7089... Generator Loss: 2.5558
Epoch 1/1... Batch 280... Discriminator Loss: 0.6969... Generator Loss: 2.8719
Epoch 1/1... Batch 290... Discriminator Loss: 0.5364... Generator Loss: 3.4481
Epoch 1/1... Batch 300... Discriminator Loss: 0.9305... Generator Loss: 1.5775
Epoch 1/1... Batch 310... Discriminator Loss: 0.6703... Generator Loss: 1.7913
Epoch 1/1... Batch 320... Discriminator Loss: 0.7498... Generator Loss: 1.6732
Epoch 1/1... Batch 330... Discriminator Loss: 0.7057... Generator Loss: 2.1338
Epoch 1/1... Batch 340... Discriminator Loss: 0.8650... Generator Loss: 1.7581
Epoch 1/1... Batch 350... Discriminator Loss: 0.9418... Generator Loss: 3.3113
Epoch 1/1... Batch 360... Discriminator Loss: 1.0347... Generator Loss: 1.0020
Epoch 1/1... Batch 370... Discriminator Loss: 1.6831... Generator Loss: 0.4537
Epoch 1/1... Batch 380... Discriminator Loss: 1.2412... Generator Loss: 0.7391
Epoch 1/1... Batch 390... Discriminator Loss: 1.0260... Generator Loss: 1.1702
Epoch 1/1... Batch 400... Discriminator Loss: 0.9258... Generator Loss: 1.2065
Epoch 1/1... Batch 410... Discriminator Loss: 0.9905... Generator Loss: 1.0101
Epoch 1/1... Batch 420... Discriminator Loss: 0.8435... Generator Loss: 1.3633
Epoch 1/1... Batch 430... Discriminator Loss: 0.6377... Generator Loss: 1.9433
Epoch 1/1... Batch 440... Discriminator Loss: 1.0299... Generator Loss: 0.9209
Epoch 1/1... Batch 450... Discriminator Loss: 0.9256... Generator Loss: 1.4475
Epoch 1/1... Batch 460... Discriminator Loss: 0.9216... Generator Loss: 2.1177
Epoch 1/1... Batch 470... Discriminator Loss: 0.8383... Generator Loss: 1.6135
Epoch 1/1... Batch 480... Discriminator Loss: 0.7114... Generator Loss: 1.9692
Epoch 1/1... Batch 490... Discriminator Loss: 0.6806... Generator Loss: 1.6929
Epoch 1/1... Batch 500... Discriminator Loss: 0.8507... Generator Loss: 1.2171
Epoch 1/1... Batch 510... Discriminator Loss: 1.3027... Generator Loss: 0.9318
Epoch 1/1... Batch 520... Discriminator Loss: 1.1808... Generator Loss: 1.7527
Epoch 1/1... Batch 530... Discriminator Loss: 0.8923... Generator Loss: 1.5419
Epoch 1/1... Batch 540... Discriminator Loss: 0.8219... Generator Loss: 1.2854
Epoch 1/1... Batch 550... Discriminator Loss: 0.7022... Generator Loss: 1.6011
Epoch 1/1... Batch 560... Discriminator Loss: 1.4315... Generator Loss: 4.7150
Epoch 1/1... Batch 570... Discriminator Loss: 0.9928... Generator Loss: 1.1479
Epoch 1/1... Batch 580... Discriminator Loss: 0.7459... Generator Loss: 1.7375
Epoch 1/1... Batch 590... Discriminator Loss: 0.8728... Generator Loss: 1.0783
Epoch 1/1... Batch 600... Discriminator Loss: 0.8745... Generator Loss: 1.1763
Epoch 1/1... Batch 610... Discriminator Loss: 0.7349... Generator Loss: 2.0993
Epoch 1/1... Batch 620... Discriminator Loss: 0.9258... Generator Loss: 1.3610
Epoch 1/1... Batch 630... Discriminator Loss: 0.7622... Generator Loss: 1.6181
Epoch 1/1... Batch 640... Discriminator Loss: 1.0719... Generator Loss: 0.9387
Epoch 1/1... Batch 650... Discriminator Loss: 0.6860... Generator Loss: 1.8406
Epoch 1/1... Batch 660... Discriminator Loss: 0.8393... Generator Loss: 1.4927
Epoch 1/1... Batch 670... Discriminator Loss: 0.7779... Generator Loss: 1.8695
Epoch 1/1... Batch 680... Discriminator Loss: 1.1662... Generator Loss: 0.8291
Epoch 1/1... Batch 690... Discriminator Loss: 0.8089... Generator Loss: 1.4982
Epoch 1/1... Batch 700... Discriminator Loss: 1.4589... Generator Loss: 0.7147
Epoch 1/1... Batch 710... Discriminator Loss: 0.9330... Generator Loss: 1.3925
Epoch 1/1... Batch 720... Discriminator Loss: 0.7049... Generator Loss: 2.1760
Epoch 1/1... Batch 730... Discriminator Loss: 0.8229... Generator Loss: 1.4217
Epoch 1/1... Batch 740... Discriminator Loss: 0.8137... Generator Loss: 1.4509
Epoch 1/1... Batch 750... Discriminator Loss: 0.7123... Generator Loss: 1.8937
Epoch 1/1... Batch 760... Discriminator Loss: 0.8115... Generator Loss: 2.0524
Epoch 1/1... Batch 770... Discriminator Loss: 1.0527... Generator Loss: 1.3715
Epoch 1/1... Batch 780... Discriminator Loss: 1.2355... Generator Loss: 0.6498
Epoch 1/1... Batch 790... Discriminator Loss: 1.0388... Generator Loss: 1.0880
Epoch 1/1... Batch 800... Discriminator Loss: 0.9857... Generator Loss: 1.0774
Epoch 1/1... Batch 810... Discriminator Loss: 0.9304... Generator Loss: 1.1437
Epoch 1/1... Batch 820... Discriminator Loss: 0.8848... Generator Loss: 1.2561
Epoch 1/1... Batch 830... Discriminator Loss: 0.7963... Generator Loss: 1.4491
Epoch 1/1... Batch 840... Discriminator Loss: 0.9776... Generator Loss: 1.0417
Epoch 1/1... Batch 850... Discriminator Loss: 1.0040... Generator Loss: 1.0997
Epoch 1/1... Batch 860... Discriminator Loss: 0.8537... Generator Loss: 1.6145
Epoch 1/1... Batch 870... Discriminator Loss: 0.9042... Generator Loss: 1.3915
Epoch 1/1... Batch 880... Discriminator Loss: 0.8221... Generator Loss: 2.9491
Epoch 1/1... Batch 890... Discriminator Loss: 0.8346... Generator Loss: 1.2183
Epoch 1/1... Batch 900... Discriminator Loss: 1.0942... Generator Loss: 2.5855
Epoch 1/1... Batch 910... Discriminator Loss: 0.8824... Generator Loss: 1.7689
Epoch 1/1... Batch 920... Discriminator Loss: 0.9683... Generator Loss: 1.3233
Epoch 1/1... Batch 930... Discriminator Loss: 1.1393... Generator Loss: 0.8168
Epoch 1/1... Batch 940... Discriminator Loss: 0.8686... Generator Loss: 1.4684
Epoch 1/1... Batch 950... Discriminator Loss: 0.7529... Generator Loss: 1.5662
Epoch 1/1... Batch 960... Discriminator Loss: 0.7247... Generator Loss: 1.4655
Epoch 1/1... Batch 970... Discriminator Loss: 0.8103... Generator Loss: 1.2879
Epoch 1/1... Batch 980... Discriminator Loss: 0.9396... Generator Loss: 2.2330
Epoch 1/1... Batch 990... Discriminator Loss: 0.9417... Generator Loss: 2.1098
Epoch 1/1... Batch 1000... Discriminator Loss: 0.7432... Generator Loss: 1.9178
Epoch 1/1... Batch 1010... Discriminator Loss: 0.7118... Generator Loss: 2.1751
Epoch 1/1... Batch 1020... Discriminator Loss: 1.0589... Generator Loss: 0.9486
Epoch 1/1... Batch 1030... Discriminator Loss: 2.0249... Generator Loss: 0.3470
Epoch 1/1... Batch 1040... Discriminator Loss: 0.8603... Generator Loss: 1.7539
Epoch 1/1... Batch 1050... Discriminator Loss: 1.1536... Generator Loss: 0.9738
Epoch 1/1... Batch 1060... Discriminator Loss: 0.8252... Generator Loss: 2.3519
Epoch 1/1... Batch 1070... Discriminator Loss: 0.8233... Generator Loss: 2.0347
Epoch 1/1... Batch 1080... Discriminator Loss: 0.8214... Generator Loss: 1.6990
Epoch 1/1... Batch 1090... Discriminator Loss: 0.6558... Generator Loss: 1.9937
Epoch 1/1... Batch 1100... Discriminator Loss: 1.0635... Generator Loss: 1.2250
Epoch 1/1... Batch 1110... Discriminator Loss: 1.0324... Generator Loss: 1.0102
Epoch 1/1... Batch 1120... Discriminator Loss: 0.7160... Generator Loss: 1.7267
Epoch 1/1... Batch 1130... Discriminator Loss: 1.0781... Generator Loss: 0.8796
Epoch 1/1... Batch 1140... Discriminator Loss: 1.1305... Generator Loss: 1.7246
Epoch 1/1... Batch 1150... Discriminator Loss: 0.7453... Generator Loss: 2.4594
Epoch 1/1... Batch 1160... Discriminator Loss: 1.0815... Generator Loss: 0.8032
Epoch 1/1... Batch 1170... Discriminator Loss: 0.6769... Generator Loss: 2.4033
Epoch 1/1... Batch 1180... Discriminator Loss: 1.0308... Generator Loss: 1.0183
Epoch 1/1... Batch 1190... Discriminator Loss: 1.2029... Generator Loss: 1.4992
Epoch 1/1... Batch 1200... Discriminator Loss: 0.8491... Generator Loss: 1.7987
Epoch 1/1... Batch 1210... Discriminator Loss: 1.0501... Generator Loss: 1.2306
Epoch 1/1... Batch 1220... Discriminator Loss: 1.0109... Generator Loss: 1.4997
Epoch 1/1... Batch 1230... Discriminator Loss: 0.7963... Generator Loss: 1.4816
Epoch 1/1... Batch 1240... Discriminator Loss: 0.7376... Generator Loss: 1.7884
Epoch 1/1... Batch 1250... Discriminator Loss: 0.9974... Generator Loss: 1.0720
Epoch 1/1... Batch 1260... Discriminator Loss: 0.9397... Generator Loss: 1.1840
Epoch 1/1... Batch 1270... Discriminator Loss: 0.9053... Generator Loss: 1.5974
Epoch 1/1... Batch 1280... Discriminator Loss: 1.2973... Generator Loss: 0.7405
Epoch 1/1... Batch 1290... Discriminator Loss: 1.0286... Generator Loss: 1.1548
Epoch 1/1... Batch 1300... Discriminator Loss: 0.9502... Generator Loss: 1.0587
Epoch 1/1... Batch 1310... Discriminator Loss: 1.1675... Generator Loss: 0.9920
Epoch 1/1... Batch 1320... Discriminator Loss: 0.9146... Generator Loss: 1.5065
Epoch 1/1... Batch 1330... Discriminator Loss: 1.4960... Generator Loss: 0.5080
Epoch 1/1... Batch 1340... Discriminator Loss: 1.0511... Generator Loss: 0.9767
Epoch 1/1... Batch 1350... Discriminator Loss: 1.0184... Generator Loss: 1.0286
Epoch 1/1... Batch 1360... Discriminator Loss: 0.8921... Generator Loss: 1.1014
Epoch 1/1... Batch 1370... Discriminator Loss: 0.9740... Generator Loss: 1.2470
Epoch 1/1... Batch 1380... Discriminator Loss: 0.8346... Generator Loss: 1.4359
Epoch 1/1... Batch 1390... Discriminator Loss: 0.8786... Generator Loss: 1.3588
Epoch 1/1... Batch 1400... Discriminator Loss: 0.8438... Generator Loss: 2.0530
Epoch 1/1... Batch 1410... Discriminator Loss: 0.8984... Generator Loss: 1.3336
Epoch 1/1... Batch 1420... Discriminator Loss: 0.9240... Generator Loss: 1.5933
Epoch 1/1... Batch 1430... Discriminator Loss: 0.8936... Generator Loss: 1.3184
Epoch 1/1... Batch 1440... Discriminator Loss: 0.8361... Generator Loss: 1.5913
Epoch 1/1... Batch 1450... Discriminator Loss: 0.8966... Generator Loss: 1.3497
Epoch 1/1... Batch 1460... Discriminator Loss: 1.2641... Generator Loss: 0.6096
Epoch 1/1... Batch 1470... Discriminator Loss: 1.0792... Generator Loss: 1.0484
Epoch 1/1... Batch 1480... Discriminator Loss: 1.0664... Generator Loss: 1.4629
Epoch 1/1... Batch 1490... Discriminator Loss: 1.0474... Generator Loss: 0.9437
Epoch 1/1... Batch 1500... Discriminator Loss: 0.8466... Generator Loss: 1.4138
Epoch 1/1... Batch 1510... Discriminator Loss: 1.3090... Generator Loss: 1.7426
Epoch 1/1... Batch 1520... Discriminator Loss: 1.1516... Generator Loss: 0.9640
Epoch 1/1... Batch 1530... Discriminator Loss: 1.0114... Generator Loss: 0.9421
Epoch 1/1... Batch 1540... Discriminator Loss: 0.9412... Generator Loss: 1.3610
Epoch 1/1... Batch 1550... Discriminator Loss: 0.8725... Generator Loss: 1.2781
Epoch 1/1... Batch 1560... Discriminator Loss: 0.8783... Generator Loss: 1.3618
Epoch 1/1... Batch 1570... Discriminator Loss: 1.0906... Generator Loss: 0.9710
Epoch 1/1... Batch 1580... Discriminator Loss: 1.1416... Generator Loss: 0.8208
Epoch 1/1... Batch 1590... Discriminator Loss: 1.1410... Generator Loss: 0.9397
Epoch 1/1... Batch 1600... Discriminator Loss: 1.3064... Generator Loss: 0.6573
Epoch 1/1... Batch 1610... Discriminator Loss: 0.8832... Generator Loss: 1.6215
Epoch 1/1... Batch 1620... Discriminator Loss: 0.8617... Generator Loss: 1.7857
Epoch 1/1... Batch 1630... Discriminator Loss: 0.8782... Generator Loss: 1.2107
Epoch 1/1... Batch 1640... Discriminator Loss: 0.9937... Generator Loss: 0.9788
Epoch 1/1... Batch 1650... Discriminator Loss: 1.0310... Generator Loss: 1.1701
Epoch 1/1... Batch 1660... Discriminator Loss: 0.8846... Generator Loss: 1.8410
Epoch 1/1... Batch 1670... Discriminator Loss: 1.0235... Generator Loss: 1.1272
Epoch 1/1... Batch 1680... Discriminator Loss: 0.9206... Generator Loss: 1.6669
Epoch 1/1... Batch 1690... Discriminator Loss: 0.7676... Generator Loss: 1.3642
Epoch 1/1... Batch 1700... Discriminator Loss: 0.8804... Generator Loss: 1.2649
Epoch 1/1... Batch 1710... Discriminator Loss: 1.0976... Generator Loss: 2.3183
Epoch 1/1... Batch 1720... Discriminator Loss: 1.1135... Generator Loss: 0.9601
Epoch 1/1... Batch 1730... Discriminator Loss: 0.8407... Generator Loss: 2.0898
Epoch 1/1... Batch 1740... Discriminator Loss: 1.1956... Generator Loss: 0.9107
Epoch 1/1... Batch 1750... Discriminator Loss: 0.7238... Generator Loss: 1.5631
Epoch 1/1... Batch 1760... Discriminator Loss: 1.0468... Generator Loss: 2.5844
Epoch 1/1... Batch 1770... Discriminator Loss: 0.9851... Generator Loss: 1.2689
Epoch 1/1... Batch 1780... Discriminator Loss: 1.1311... Generator Loss: 1.0212
Epoch 1/1... Batch 1790... Discriminator Loss: 0.9403... Generator Loss: 1.3741
Epoch 1/1... Batch 1800... Discriminator Loss: 0.8042... Generator Loss: 1.4883
Epoch 1/1... Batch 1810... Discriminator Loss: 0.9658... Generator Loss: 1.5760
Epoch 1/1... Batch 1820... Discriminator Loss: 0.8572... Generator Loss: 1.3186
Epoch 1/1... Batch 1830... Discriminator Loss: 1.0934... Generator Loss: 0.9982
Epoch 1/1... Batch 1840... Discriminator Loss: 1.0950... Generator Loss: 1.2071
Epoch 1/1... Batch 1850... Discriminator Loss: 1.5164... Generator Loss: 0.5252
Epoch 1/1... Batch 1860... Discriminator Loss: 0.8326... Generator Loss: 1.2613
Epoch 1/1... Batch 1870... Discriminator Loss: 0.9005... Generator Loss: 1.2722
Epoch 1/1... Batch 1880... Discriminator Loss: 0.9755... Generator Loss: 1.1864
Epoch 1/1... Batch 1890... Discriminator Loss: 1.0907... Generator Loss: 1.0982
Epoch 1/1... Batch 1900... Discriminator Loss: 1.0982... Generator Loss: 0.9848
Epoch 1/1... Batch 1910... Discriminator Loss: 0.8510... Generator Loss: 1.4081
Epoch 1/1... Batch 1920... Discriminator Loss: 1.0050... Generator Loss: 1.1998
Epoch 1/1... Batch 1930... Discriminator Loss: 0.9713... Generator Loss: 1.0218
Epoch 1/1... Batch 1940... Discriminator Loss: 1.0214... Generator Loss: 1.1437
Epoch 1/1... Batch 1950... Discriminator Loss: 0.9227... Generator Loss: 1.9675
Epoch 1/1... Batch 1960... Discriminator Loss: 1.9184... Generator Loss: 0.2930
Epoch 1/1... Batch 1970... Discriminator Loss: 0.9937... Generator Loss: 1.4005
Epoch 1/1... Batch 1980... Discriminator Loss: 0.9587... Generator Loss: 1.2957
Epoch 1/1... Batch 1990... Discriminator Loss: 1.0737... Generator Loss: 1.5363
Epoch 1/1... Batch 2000... Discriminator Loss: 0.8391... Generator Loss: 1.7913
Epoch 1/1... Batch 2010... Discriminator Loss: 0.8712... Generator Loss: 1.3185
Epoch 1/1... Batch 2020... Discriminator Loss: 0.9176... Generator Loss: 1.3252
Epoch 1/1... Batch 2030... Discriminator Loss: 1.0147... Generator Loss: 1.1826
Epoch 1/1... Batch 2040... Discriminator Loss: 0.9230... Generator Loss: 1.3131
Epoch 1/1... Batch 2050... Discriminator Loss: 1.1914... Generator Loss: 0.6753
Epoch 1/1... Batch 2060... Discriminator Loss: 0.7827... Generator Loss: 1.6399
Epoch 1/1... Batch 2070... Discriminator Loss: 0.8896... Generator Loss: 1.6641
Epoch 1/1... Batch 2080... Discriminator Loss: 0.8709... Generator Loss: 1.4295
Epoch 1/1... Batch 2090... Discriminator Loss: 1.0077... Generator Loss: 1.1498
Epoch 1/1... Batch 2100... Discriminator Loss: 0.9127... Generator Loss: 1.4570
Epoch 1/1... Batch 2110... Discriminator Loss: 0.8321... Generator Loss: 1.7270
Epoch 1/1... Batch 2120... Discriminator Loss: 0.9618... Generator Loss: 1.1110
Epoch 1/1... Batch 2130... Discriminator Loss: 0.9770... Generator Loss: 1.0849
Epoch 1/1... Batch 2140... Discriminator Loss: 1.1799... Generator Loss: 0.9227
Epoch 1/1... Batch 2150... Discriminator Loss: 0.9422... Generator Loss: 1.8898
Epoch 1/1... Batch 2160... Discriminator Loss: 0.9524... Generator Loss: 1.5290
Epoch 1/1... Batch 2170... Discriminator Loss: 1.1289... Generator Loss: 0.7714
Epoch 1/1... Batch 2180... Discriminator Loss: 1.0168... Generator Loss: 1.0782
Epoch 1/1... Batch 2190... Discriminator Loss: 0.9573... Generator Loss: 1.1140
Epoch 1/1... Batch 2200... Discriminator Loss: 1.1049... Generator Loss: 1.0927
Epoch 1/1... Batch 2210... Discriminator Loss: 1.0369... Generator Loss: 1.4437
Epoch 1/1... Batch 2220... Discriminator Loss: 0.9264... Generator Loss: 1.2718
Epoch 1/1... Batch 2230... Discriminator Loss: 1.0007... Generator Loss: 1.1241
Epoch 1/1... Batch 2240... Discriminator Loss: 0.8687... Generator Loss: 1.9800
Epoch 1/1... Batch 2250... Discriminator Loss: 1.1178... Generator Loss: 0.8284
Epoch 1/1... Batch 2260... Discriminator Loss: 0.8808... Generator Loss: 1.4441
Epoch 1/1... Batch 2270... Discriminator Loss: 0.8707... Generator Loss: 1.5946
Epoch 1/1... Batch 2280... Discriminator Loss: 0.8189... Generator Loss: 1.3955
Epoch 1/1... Batch 2290... Discriminator Loss: 1.2649... Generator Loss: 2.0867
Epoch 1/1... Batch 2300... Discriminator Loss: 0.9333... Generator Loss: 1.1581
Epoch 1/1... Batch 2310... Discriminator Loss: 0.9166... Generator Loss: 1.4896
Epoch 1/1... Batch 2320... Discriminator Loss: 0.8875... Generator Loss: 1.2692
Epoch 1/1... Batch 2330... Discriminator Loss: 1.1943... Generator Loss: 0.7734
Epoch 1/1... Batch 2340... Discriminator Loss: 0.9561... Generator Loss: 1.1158
Epoch 1/1... Batch 2350... Discriminator Loss: 1.1613... Generator Loss: 0.8878
Epoch 1/1... Batch 2360... Discriminator Loss: 1.0767... Generator Loss: 1.0607
Epoch 1/1... Batch 2370... Discriminator Loss: 0.8981... Generator Loss: 1.4554
Epoch 1/1... Batch 2380... Discriminator Loss: 1.0324... Generator Loss: 1.1603
Epoch 1/1... Batch 2390... Discriminator Loss: 0.9088... Generator Loss: 1.1455
Epoch 1/1... Batch 2400... Discriminator Loss: 1.1075... Generator Loss: 1.0319
Epoch 1/1... Batch 2410... Discriminator Loss: 1.1031... Generator Loss: 0.8325
Epoch 1/1... Batch 2420... Discriminator Loss: 1.0308... Generator Loss: 1.0902
Epoch 1/1... Batch 2430... Discriminator Loss: 0.9552... Generator Loss: 1.4281
Epoch 1/1... Batch 2440... Discriminator Loss: 0.9974... Generator Loss: 1.1346
Epoch 1/1... Batch 2450... Discriminator Loss: 0.9348... Generator Loss: 1.1726
Epoch 1/1... Batch 2460... Discriminator Loss: 0.9891... Generator Loss: 1.1064
Epoch 1/1... Batch 2470... Discriminator Loss: 0.9004... Generator Loss: 1.2093
Epoch 1/1... Batch 2480... Discriminator Loss: 0.9721... Generator Loss: 1.0325
Epoch 1/1... Batch 2490... Discriminator Loss: 0.9043... Generator Loss: 1.5804
Epoch 1/1... Batch 2500... Discriminator Loss: 0.8357... Generator Loss: 1.8937
Epoch 1/1... Batch 2510... Discriminator Loss: 0.9691... Generator Loss: 2.2030
Epoch 1/1... Batch 2520... Discriminator Loss: 1.1169... Generator Loss: 1.1290
Epoch 1/1... Batch 2530... Discriminator Loss: 0.9248... Generator Loss: 1.3738
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Epoch 1/1... Batch 5180... Discriminator Loss: 1.0249... Generator Loss: 0.9273
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Epoch 1/1... Batch 5200... Discriminator Loss: 1.1526... Generator Loss: 1.0760
Epoch 1/1... Batch 5210... Discriminator Loss: 1.1035... Generator Loss: 1.0478
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Epoch 1/1... Batch 5250... Discriminator Loss: 1.0105... Generator Loss: 1.5208
Epoch 1/1... Batch 5260... Discriminator Loss: 1.2473... Generator Loss: 0.7320
Epoch 1/1... Batch 5270... Discriminator Loss: 0.8472... Generator Loss: 1.3167
Epoch 1/1... Batch 5280... Discriminator Loss: 0.8742... Generator Loss: 1.2027
Epoch 1/1... Batch 5290... Discriminator Loss: 1.1958... Generator Loss: 0.9247
Epoch 1/1... Batch 5300... Discriminator Loss: 0.8495... Generator Loss: 1.2850
Epoch 1/1... Batch 5310... Discriminator Loss: 1.1892... Generator Loss: 1.0928
Epoch 1/1... Batch 5320... Discriminator Loss: 0.9656... Generator Loss: 0.9666
Epoch 1/1... Batch 5330... Discriminator Loss: 0.9245... Generator Loss: 1.1638
Epoch 1/1... Batch 5340... Discriminator Loss: 1.1138... Generator Loss: 0.9928
Epoch 1/1... Batch 5350... Discriminator Loss: 0.9643... Generator Loss: 1.1718
Epoch 1/1... Batch 5360... Discriminator Loss: 0.8568... Generator Loss: 1.5543
Epoch 1/1... Batch 5370... Discriminator Loss: 1.0569... Generator Loss: 0.9261
Epoch 1/1... Batch 5380... Discriminator Loss: 0.9164... Generator Loss: 1.4404
Epoch 1/1... Batch 5390... Discriminator Loss: 0.9261... Generator Loss: 1.3402
Epoch 1/1... Batch 5400... Discriminator Loss: 1.1239... Generator Loss: 0.8693
Epoch 1/1... Batch 5410... Discriminator Loss: 1.2868... Generator Loss: 1.1957
Epoch 1/1... Batch 5420... Discriminator Loss: 1.0044... Generator Loss: 1.1428
Epoch 1/1... Batch 5430... Discriminator Loss: 1.0953... Generator Loss: 0.8982
Epoch 1/1... Batch 5440... Discriminator Loss: 0.8752... Generator Loss: 1.3700
Epoch 1/1... Batch 5450... Discriminator Loss: 0.9167... Generator Loss: 1.1396
Epoch 1/1... Batch 5460... Discriminator Loss: 1.1023... Generator Loss: 0.7624
Epoch 1/1... Batch 5470... Discriminator Loss: 1.0215... Generator Loss: 1.0200
Epoch 1/1... Batch 5480... Discriminator Loss: 0.9391... Generator Loss: 1.3068
Epoch 1/1... Batch 5490... Discriminator Loss: 1.0952... Generator Loss: 0.8373
Epoch 1/1... Batch 5500... Discriminator Loss: 1.2555... Generator Loss: 0.7382
Epoch 1/1... Batch 5510... Discriminator Loss: 1.1144... Generator Loss: 0.9115
Epoch 1/1... Batch 5520... Discriminator Loss: 1.1309... Generator Loss: 0.8989
Epoch 1/1... Batch 5530... Discriminator Loss: 1.3856... Generator Loss: 0.5609
Epoch 1/1... Batch 5540... Discriminator Loss: 1.0579... Generator Loss: 0.9296
Epoch 1/1... Batch 5550... Discriminator Loss: 1.0585... Generator Loss: 0.9236
Epoch 1/1... Batch 5560... Discriminator Loss: 1.1533... Generator Loss: 0.8059
Epoch 1/1... Batch 5570... Discriminator Loss: 1.1295... Generator Loss: 0.9769
Epoch 1/1... Batch 5580... Discriminator Loss: 1.0851... Generator Loss: 0.8799
Epoch 1/1... Batch 5590... Discriminator Loss: 1.4170... Generator Loss: 0.5417
Epoch 1/1... Batch 5600... Discriminator Loss: 1.0283... Generator Loss: 0.9497
Epoch 1/1... Batch 5610... Discriminator Loss: 1.0710... Generator Loss: 0.9498
Epoch 1/1... Batch 5620... Discriminator Loss: 0.9726... Generator Loss: 1.2358
Epoch 1/1... Batch 5630... Discriminator Loss: 0.9790... Generator Loss: 1.1113
Epoch 1/1... Batch 5640... Discriminator Loss: 0.9819... Generator Loss: 0.9893
Epoch 1/1... Batch 5650... Discriminator Loss: 1.1353... Generator Loss: 0.7444
Epoch 1/1... Batch 5660... Discriminator Loss: 1.1291... Generator Loss: 0.8368
Epoch 1/1... Batch 5670... Discriminator Loss: 1.0054... Generator Loss: 1.0380
Epoch 1/1... Batch 5680... Discriminator Loss: 1.0167... Generator Loss: 1.0913
Epoch 1/1... Batch 5690... Discriminator Loss: 1.0529... Generator Loss: 1.0240
Epoch 1/1... Batch 5700... Discriminator Loss: 1.1110... Generator Loss: 1.2347
Epoch 1/1... Batch 5710... Discriminator Loss: 1.1620... Generator Loss: 1.5292
Epoch 1/1... Batch 5720... Discriminator Loss: 0.8549... Generator Loss: 1.1376
Epoch 1/1... Batch 5730... Discriminator Loss: 1.1248... Generator Loss: 0.9793
Epoch 1/1... Batch 5740... Discriminator Loss: 1.1860... Generator Loss: 0.7564
Epoch 1/1... Batch 5750... Discriminator Loss: 1.0861... Generator Loss: 0.9983
Epoch 1/1... Batch 5760... Discriminator Loss: 1.1127... Generator Loss: 0.9570
Epoch 1/1... Batch 5770... Discriminator Loss: 1.0323... Generator Loss: 0.9646
Epoch 1/1... Batch 5780... Discriminator Loss: 1.0477... Generator Loss: 0.9887
Epoch 1/1... Batch 5790... Discriminator Loss: 1.3262... Generator Loss: 0.6188
Epoch 1/1... Batch 5800... Discriminator Loss: 1.1286... Generator Loss: 0.9366
Epoch 1/1... Batch 5810... Discriminator Loss: 1.0015... Generator Loss: 1.1241
Epoch 1/1... Batch 5820... Discriminator Loss: 1.0123... Generator Loss: 1.1277
Epoch 1/1... Batch 5830... Discriminator Loss: 0.9519... Generator Loss: 1.0764
Epoch 1/1... Batch 5840... Discriminator Loss: 1.1045... Generator Loss: 0.9258
Epoch 1/1... Batch 5850... Discriminator Loss: 1.0769... Generator Loss: 0.8638
Epoch 1/1... Batch 5860... Discriminator Loss: 0.9837... Generator Loss: 1.1229
Epoch 1/1... Batch 5870... Discriminator Loss: 1.1600... Generator Loss: 0.7353
Epoch 1/1... Batch 5880... Discriminator Loss: 0.9337... Generator Loss: 1.1641
Epoch 1/1... Batch 5890... Discriminator Loss: 1.0354... Generator Loss: 0.8007
Epoch 1/1... Batch 5900... Discriminator Loss: 0.9257... Generator Loss: 1.1056
Epoch 1/1... Batch 5910... Discriminator Loss: 1.2023... Generator Loss: 0.7102
Epoch 1/1... Batch 5920... Discriminator Loss: 1.1081... Generator Loss: 0.8095
Epoch 1/1... Batch 5930... Discriminator Loss: 1.1532... Generator Loss: 0.9501
Epoch 1/1... Batch 5940... Discriminator Loss: 1.1426... Generator Loss: 0.8439
Epoch 1/1... Batch 5950... Discriminator Loss: 1.3372... Generator Loss: 0.6436
Epoch 1/1... Batch 5960... Discriminator Loss: 1.1957... Generator Loss: 0.8541
Epoch 1/1... Batch 5970... Discriminator Loss: 1.0453... Generator Loss: 1.1126
Epoch 1/1... Batch 5980... Discriminator Loss: 1.1228... Generator Loss: 0.9065
Epoch 1/1... Batch 5990... Discriminator Loss: 1.0799... Generator Loss: 0.8483
Epoch 1/1... Batch 6000... Discriminator Loss: 0.8982... Generator Loss: 1.6054
Epoch 1/1... Batch 6010... Discriminator Loss: 1.1107... Generator Loss: 0.9125
Epoch 1/1... Batch 6020... Discriminator Loss: 1.0182... Generator Loss: 1.2815
Epoch 1/1... Batch 6030... Discriminator Loss: 1.0376... Generator Loss: 0.9992
Epoch 1/1... Batch 6040... Discriminator Loss: 0.9682... Generator Loss: 1.1829
Epoch 1/1... Batch 6050... Discriminator Loss: 1.2969... Generator Loss: 0.6844
Epoch 1/1... Batch 6060... Discriminator Loss: 0.9386... Generator Loss: 1.2400
Epoch 1/1... Batch 6070... Discriminator Loss: 0.9807... Generator Loss: 1.4936
Epoch 1/1... Batch 6080... Discriminator Loss: 1.0954... Generator Loss: 0.8250
Epoch 1/1... Batch 6090... Discriminator Loss: 1.0418... Generator Loss: 0.8780
Epoch 1/1... Batch 6100... Discriminator Loss: 1.1485... Generator Loss: 1.3562
Epoch 1/1... Batch 6110... Discriminator Loss: 1.0208... Generator Loss: 1.0970
Epoch 1/1... Batch 6120... Discriminator Loss: 0.8523... Generator Loss: 1.1794
Epoch 1/1... Batch 6130... Discriminator Loss: 1.1938... Generator Loss: 0.8465
Epoch 1/1... Batch 6140... Discriminator Loss: 1.1414... Generator Loss: 0.7014
Epoch 1/1... Batch 6150... Discriminator Loss: 1.1993... Generator Loss: 0.7989
Epoch 1/1... Batch 6160... Discriminator Loss: 1.0010... Generator Loss: 1.0484
Epoch 1/1... Batch 6170... Discriminator Loss: 0.9696... Generator Loss: 1.0270
Epoch 1/1... Batch 6180... Discriminator Loss: 0.9132... Generator Loss: 1.1599
Epoch 1/1... Batch 6190... Discriminator Loss: 1.1801... Generator Loss: 0.9948
Epoch 1/1... Batch 6200... Discriminator Loss: 0.9860... Generator Loss: 1.1749
Epoch 1/1... Batch 6210... Discriminator Loss: 1.4736... Generator Loss: 0.4707
Epoch 1/1... Batch 6220... Discriminator Loss: 1.0019... Generator Loss: 1.1790
Epoch 1/1... Batch 6230... Discriminator Loss: 1.0007... Generator Loss: 0.9623
Epoch 1/1... Batch 6240... Discriminator Loss: 0.8934... Generator Loss: 1.6620
Epoch 1/1... Batch 6250... Discriminator Loss: 1.2524... Generator Loss: 0.8285
Epoch 1/1... Batch 6260... Discriminator Loss: 1.0421... Generator Loss: 0.9692
Epoch 1/1... Batch 6270... Discriminator Loss: 1.1458... Generator Loss: 0.9666
Epoch 1/1... Batch 6280... Discriminator Loss: 0.9017... Generator Loss: 1.3491
Epoch 1/1... Batch 6290... Discriminator Loss: 0.9701... Generator Loss: 1.0670
Epoch 1/1... Batch 6300... Discriminator Loss: 1.4361... Generator Loss: 0.5266
Epoch 1/1... Batch 6310... Discriminator Loss: 0.9778... Generator Loss: 1.5465
Epoch 1/1... Batch 6320... Discriminator Loss: 1.0929... Generator Loss: 1.0061
Epoch 1/1... Batch 6330... Discriminator Loss: 0.9900... Generator Loss: 1.3636

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.